In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available:this https URL.
View on arXiv@article{reichert2025_2504.21602,
title={ Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans },
author={ Hannes Reichert and Benjamin Serfling and Elijah Schüssler and Kerim Turacan and Konrad Doll and Bernhard Sick },
journal={arXiv preprint arXiv:2504.21602},
year={ 2025 }
}